Introduction
In the early hours of a quiet weekend, Andrej Karpathy—once Tesla’s AI director and a founding hand behind OpenAI—decided to read a book, but not in isolation. He wanted a committee of artificial intelligences to read alongside him, each offering a distinct perspective, critiquing one another, and finally synthesizing a single, authoritative answer under the guidance of a “Chairman.” The result was a playful yet revealing piece of software, the LLM Council, that Karpathy posted to GitHub with a candid disclaimer that he would not support it. Beneath the surface of this weekend toy lies a surprisingly sophisticated reference architecture for a layer that has long been missing from the modern software stack: the orchestration middleware that sits between corporate applications and the volatile market of AI models. As enterprises lock in platform choices for 2026, the LLM Council offers a stripped‑down view of the “build vs. buy” dilemma in AI infrastructure, showing that while routing and aggregating models is straightforward, the operational wrapper required to make it enterprise‑ready is where true complexity resides.
Main Content
The LLM Council Workflow
The LLM Council’s user interface looks almost identical to a conventional chatbot: a text box, a send button, and a scrolling conversation. However, behind the scenes the application executes a three‑stage workflow that mirrors the deliberative process of a human decision‑making body. First, the user’s query is dispatched in parallel to a panel of frontier models—OpenAI’s GPT‑5.1, Google’s Gemini 3.0 Pro, Anthropic’s Claude Sonnet 4.5, and xAI’s Grok 4. Each model generates its own response without knowledge of the others. In the second stage, the system performs a peer review: every model receives the anonymized responses of its counterparts and is asked to evaluate them on accuracy and insight. This transforms the AI from a simple generator into a critic, adding a layer of quality control that is rarely present in standard chatbot interactions. Finally, a designated “Chairman LLM,” currently configured as Gemini 3, receives the original query, the individual responses, and the peer rankings. It synthesizes this mass of context into a single, authoritative answer for the user. Karpathy noted that the results were often surprising; the models frequently praised GPT‑5.1 as the most insightful while rating Claude the lowest, yet his own qualitative assessment favored Gemini’s concise output.
Architecture Choices and Vendor Agnosticism
For CTOs and platform architects, the value of the LLM Council lies not in its literary criticism but in its construction. The repository demonstrates a minimal, “thin” AI stack that can be replicated in a few hundred lines of code. The backend is built with FastAPI, a modern Python framework, while the frontend is a React application bundled with Vite. Data storage is handled by simple JSON files written to the local disk, avoiding the complexity of a full database. The linchpin of the entire operation is OpenRouter, an API aggregator that normalizes the differences between various model providers. By routing requests through this single broker, Karpathy avoided writing separate integration code for OpenAI, Google, and Anthropic. The application does not know or care which company provides the intelligence; it simply sends a prompt and awaits a response. This design choice highlights a growing trend in enterprise architecture: the commoditization of the model layer. By treating frontier models as interchangeable components that can be swapped by editing a single line in a configuration file—specifically the COUNCIL_MODELS list in the backend code—the architecture protects the application from vendor lock‑in. If a new model from Meta or Mistral tops the leaderboards next week, it can be added to the council in seconds.
From Prototype to Production: Governance and Compliance
While the core logic of the LLM Council is elegant, it also serves as a stark illustration of the gap between a “weekend hack” and a production system. A technical audit of the code reveals the missing “boring” infrastructure that commercial vendors sell for premium prices. The system lacks authentication; anyone with access to the web interface can query the models. There is no concept of user roles, meaning a junior developer has the same access rights as the CIO. Furthermore, the governance layer is nonexistent. In a corporate environment, sending data to four different external AI providers simultaneously triggers immediate compliance concerns. There is no mechanism to redact Personally Identifiable Information (PII) before it leaves the local network, nor is there an audit log to track who asked what. Reliability is another open question. The system assumes the OpenRouter API is always up and that the models will respond in a timely fashion. It lacks the circuit breakers, fallback strategies, and retry logic that keep business‑critical applications running when a provider suffers an outage. These absences are not flaws in Karpathy’s code—he explicitly stated he does not intend to support or improve the project—but they define the value proposition for the commercial AI infrastructure market. Companies like LangChain, AWS Bedrock, and various AI gateway startups are essentially selling the “hardening” around the core logic that Karpathy demonstrated, providing the security, observability, and compliance wrappers that turn a raw orchestration script into a viable enterprise platform.
The Ephemeral Code Paradigm
Perhaps the most provocative aspect of the project is the philosophy under which it was built. Karpathy described the development process as “99% vibe‑coded,” implying he relied heavily on AI assistants to generate the code rather than writing it line‑by‑line himself. He wrote that “code is now ephemerally… libraries are over, ask your LLM to change it in whatever way you like.” This statement marks a radical shift in software engineering capability. Traditionally, companies build internal libraries and abstractions to manage complexity, maintaining them for years. Karpathy is suggesting a future where code is treated as promptable scaffolding—disposable, easily rewritten by AI, and not meant to last. For enterprise decision‑makers, this poses a difficult strategic question. If internal tools can be “vibe coded” in a weekend, does it make sense to buy expensive, rigid software suites for internal workflows? Or should platform teams empower their engineers to generate custom, disposable tools that fit their exact needs for a fraction of the cost?
Alignment Risks in AI‑as‑Judge
Beyond the architecture, the LLM Council experiment inadvertently shines a light on a specific risk in automated AI deployment: the divergence between human and machine judgment. Karpathy’s observation that his models preferred GPT‑5.1, while he preferred Gemini, suggests that AI models may have shared biases. They might favor verbosity, specific formatting, or rhetorical confidence that does not necessarily align with human business needs for brevity and accuracy. As enterprises increasingly rely on “LLM‑as‑a‑Judge” systems to evaluate the quality of their customer‑facing bots, this discrepancy matters. If the automated evaluator consistently rewards “wordy and sprawled” answers while human customers want concise solutions, the metrics will show success while customer satisfaction plummets. Karpathy’s experiment suggests that relying solely on AI to grade AI is a strategy fraught with hidden alignment issues.
Implications for 2026 Enterprise AI Platforms
Ultimately, the LLM Council acts as a Rorschach test for the AI industry. For the hobbyist, it is a fun way to read books. For the vendor, it is a threat, proving that the core functionality of their products can be replicated in a few hundred lines of code. But for the enterprise technology leader, it is a reference architecture. It demystifies the orchestration layer, showing that the technical challenge is not in routing the prompts, but in governing the data. As platform teams head into 2026, many will likely find themselves staring at Karpathy’s code, not to deploy it, but to understand it. It proves that a multi‑model strategy is not technically out of reach. The question remains whether companies will build the governance layer themselves or pay someone else to wrap the “vibe code” in enterprise‑grade armor.
Conclusion
The LLM Council may have started as a weekend experiment, but its implications ripple far beyond a playful chatbot. It exposes the missing orchestration layer that sits between corporate applications and the volatile market of AI models, revealing that the real challenge lies not in routing prompts but in building a robust governance and compliance framework. By treating models as interchangeable components and leveraging a single API broker, the project demonstrates a vendor‑agnostic approach that protects against lock‑in and enables rapid experimentation. Yet the absence of authentication, PII redaction, audit logging, and resilience mechanisms highlights the gap between a prototype and a production‑grade system. The philosophical stance that code is now “ephemeral” and that libraries are obsolete forces enterprises to rethink their approach to internal tooling, weighing the cost of building versus buying. Finally, the divergence between AI‑generated judgments and human preferences underscores the need for careful alignment and human oversight in AI‑as‑a‑Judge scenarios. In sum, the LLM Council offers a clear, pragmatic blueprint for the orchestration layer while simultaneously warning of the pitfalls that must be addressed before enterprises can safely scale AI across their platforms.
Call to Action
If you’re a platform architect, CTO, or AI enthusiast, dive into the LLM Council repository and experiment with your own model council. Use it as a sandbox to test different orchestration patterns, evaluate the impact of adding or removing models, and explore how a simple JSON‑based configuration can drive vendor‑agnostic deployments. Then, take the next step: design a governance layer that incorporates authentication, role‑based access, PII redaction, audit logging, and resilience patterns. By building or adopting these hardening components, you can transform a weekend hack into a production‑ready AI orchestration platform that meets enterprise security, compliance, and reliability standards. The future of AI integration is modular, adaptable, and governed by clear policies—start building it today.